from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
Daal4py_kmeans_short: 0h 0m 1s
Daal4py_ridge: 0h 0m 1s
Kmeans_short: 0h 0m 2s
Daal4py_logisticregression: 0h 0m 3s
Daal4py_kmeans_tall: 0h 0m 7s
Ridge: 0h 0m 10s
Logisticregression: 0h 0m 18s
Kmeans_tall: 0h 0m 21s
Daal4py_kneighborsclassifier_kd_tree: 0h 0m 25s
Daal4py_kneighborsclassifier: 0h 2m 27s
Kneighborsclassifier_kd_tree: 0h 2m 27s
Histgradientboostingclassifier: 0h 5m 1s
Catboost_symmetric: 0h 5m 2s
Catboost: 0h 5m 9s
Lightgbm: 0h 5m 10s
Xgboost: 0h 5m 13s
Kneighborsclassifier: 0h 38m 42s
Total: 1h 10m 49s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config_file_path="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.128 | 0.000 | 6.244 | 0.000 | 1 | 100 | NaN | NaN | 0.451 | 0.000 | 0.284 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 17.404 | 0.112 | 0.000 | 0.017 | 1 | 100 | 0.932 | 0.713 | 1.718 | 0.011 | 10.130 | 0.091 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.183 | 0.001 | 0.000 | 0.183 | 1 | 100 | 1.000 | 0.000 | 0.078 | 0.000 | 2.333 | 0.015 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.121 | 0.000 | 6.588 | 0.000 | 1 | 1 | NaN | NaN | 0.439 | 0.000 | 0.276 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 12.146 | 0.109 | 0.000 | 0.012 | 1 | 1 | 0.727 | 0.713 | 1.800 | 0.042 | 6.746 | 0.167 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.173 | 0.004 | 0.000 | 0.173 | 1 | 1 | 1.000 | 0.000 | 0.080 | 0.001 | 2.163 | 0.049 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.122 | 0.000 | 6.568 | 0.000 | -1 | 1 | NaN | NaN | 0.442 | 0.000 | 0.275 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.780 | 0.140 | 0.000 | 0.023 | -1 | 1 | 0.727 | 0.819 | 1.763 | 0.035 | 12.925 | 0.265 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.148 | 0.013 | 0.000 | 0.148 | -1 | 1 | 1.000 | 1.000 | 0.077 | 0.000 | 1.911 | 0.164 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.120 | 0.000 | 6.658 | 0.000 | 1 | 5 | NaN | NaN | 0.437 | 0.000 | 0.275 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 17.369 | 0.134 | 0.000 | 0.017 | 1 | 5 | 0.821 | 0.949 | 1.773 | 0.014 | 9.798 | 0.109 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.179 | 0.001 | 0.000 | 0.179 | 1 | 5 | 1.000 | 1.000 | 0.077 | 0.000 | 2.330 | 0.015 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.115 | 0.000 | 6.957 | 0.000 | -1 | 100 | NaN | NaN | 0.439 | 0.000 | 0.262 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 27.992 | 0.259 | 0.000 | 0.028 | -1 | 100 | 0.932 | 0.949 | 1.863 | 0.018 | 15.022 | 0.204 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.162 | 0.011 | 0.000 | 0.162 | -1 | 100 | 1.000 | 1.000 | 0.080 | 0.001 | 2.016 | 0.138 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.118 | 0.000 | 6.780 | 0.000 | -1 | 5 | NaN | NaN | 0.444 | 0.000 | 0.265 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 27.989 | 0.335 | 0.000 | 0.028 | -1 | 5 | 0.821 | 0.819 | 1.737 | 0.031 | 16.118 | 0.350 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.168 | 0.010 | 0.000 | 0.168 | -1 | 5 | 1.000 | 1.000 | 0.081 | 0.007 | 2.087 | 0.231 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.297 | 0.000 | 1 | 100 | NaN | NaN | 0.097 | 0.000 | 0.554 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.504 | 0.092 | 0.000 | 0.020 | 1 | 100 | 0.990 | 0.974 | 0.264 | 0.002 | 73.847 | 0.644 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.023 | 0.001 | 0.000 | 0.023 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.400 | 0.211 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.051 | 0.000 | 0.311 | 0.000 | 1 | 1 | NaN | NaN | 0.098 | 0.000 | 0.527 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 9.447 | 0.052 | 0.000 | 0.009 | 1 | 1 | 0.979 | 0.974 | 0.262 | 0.002 | 36.024 | 0.393 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.013 | 0.000 | 0.000 | 0.013 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 2.460 | 0.152 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.308 | 0.000 | -1 | 1 | NaN | NaN | 0.097 | 0.000 | 0.534 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.238 | 0.191 | 0.000 | 0.020 | -1 | 1 | 0.979 | 0.978 | 0.265 | 0.002 | 76.327 | 0.958 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.018 | 0.003 | 0.000 | 0.018 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.341 | 0.539 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.264 | 0.000 | 1 | 5 | NaN | NaN | 0.098 | 0.000 | 0.619 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.426 | 0.082 | 0.000 | 0.019 | 1 | 5 | 0.988 | 0.982 | 0.311 | 0.002 | 62.429 | 0.417 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.023 | 0.000 | 0.000 | 0.023 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 4.303 | 0.275 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.310 | 0.000 | -1 | 100 | NaN | NaN | 0.097 | 0.000 | 0.533 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.013 | 0.192 | 0.000 | 0.030 | -1 | 100 | 0.990 | 0.982 | 0.310 | 0.004 | 96.773 | 1.457 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.001 | 0.000 | 0.028 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 5.317 | 0.411 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.305 | 0.000 | -1 | 5 | NaN | NaN | 0.096 | 0.000 | 0.545 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.435 | 0.000 | 0.000 | 0.030 | -1 | 5 | 0.988 | 0.978 | 0.266 | 0.004 | 114.329 | 1.629 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.001 | 0.000 | 0.028 | -1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 5.071 | 0.344 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.017 | 0.000 | 0.027 | 0.000 | -1 | 5 | NaN | NaN | 0.668 | 0.000 | 4.517 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.791 | 0.010 | 0.000 | 0.001 | -1 | 5 | 0.973 | 0.984 | 0.524 | 0.025 | 1.508 | 0.074 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 5.356 | 2.140 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.957 | 0.000 | 0.027 | 0.000 | -1 | 100 | NaN | NaN | 0.638 | 0.000 | 4.635 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.550 | 0.019 | 0.000 | 0.003 | -1 | 100 | 0.973 | 0.982 | 0.168 | 0.003 | 15.216 | 0.275 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 15.412 | 8.143 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.824 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.649 | 0.000 | 4.351 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.404 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.959 | 0.984 | 0.501 | 0.006 | 0.806 | 0.012 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.050 | 2.186 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.795 | 0.000 | 0.029 | 0.000 | 1 | 100 | NaN | NaN | 0.634 | 0.000 | 4.408 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.368 | 0.092 | 0.000 | 0.004 | 1 | 100 | 0.973 | 0.982 | 0.165 | 0.003 | 26.451 | 0.753 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 8.543 | 4.339 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.783 | 0.000 | 0.029 | 0.000 | 1 | 1 | NaN | NaN | 0.645 | 0.000 | 4.312 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.659 | 0.004 | 0.000 | 0.001 | 1 | 1 | 0.959 | 0.962 | 0.093 | 0.004 | 7.076 | 0.316 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 6.888 | 4.526 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.824 | 0.000 | 0.028 | 0.000 | 1 | 5 | NaN | NaN | 0.633 | 0.000 | 4.460 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.322 | 0.009 | 0.000 | 0.001 | 1 | 5 | 0.973 | 0.962 | 0.091 | 0.001 | 14.559 | 0.201 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 7.879 | 5.028 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.772 | 0.000 | 0.021 | 0.000 | -1 | 5 | NaN | NaN | 0.455 | 0.000 | 1.699 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.028 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.974 | 0.979 | 0.007 | 0.001 | 4.189 | 0.435 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 18.416 | 14.273 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.790 | 0.000 | 0.020 | 0.000 | -1 | 100 | NaN | NaN | 0.465 | 0.000 | 1.700 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.045 | 0.000 | 0.000 | 0.000 | -1 | 100 | 0.978 | 0.976 | 0.001 | 0.000 | 43.760 | 11.479 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 20.034 | 14.889 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.764 | 0.000 | 0.021 | 0.000 | -1 | 1 | NaN | NaN | 0.432 | 0.000 | 1.771 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.969 | 0.979 | 0.006 | 0.001 | 4.173 | 0.461 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 23.187 | 18.169 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.828 | 0.000 | 0.019 | 0.000 | 1 | 100 | NaN | NaN | 0.432 | 0.000 | 1.915 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.054 | 0.002 | 0.000 | 0.000 | 1 | 100 | 0.978 | 0.976 | 0.001 | 0.000 | 52.715 | 15.396 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 6.142 | 4.667 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.754 | 0.000 | 0.021 | 0.000 | 1 | 1 | NaN | NaN | 0.433 | 0.000 | 1.742 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.969 | 0.971 | 0.001 | 0.000 | 33.026 | 12.276 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.839 | 4.794 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.749 | 0.000 | 0.021 | 0.000 | 1 | 5 | NaN | NaN | 0.433 | 0.000 | 1.727 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.974 | 0.971 | 0.001 | 0.000 | 37.253 | 13.648 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.864 | 4.709 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.592 | 0.000 | 0.811 | 0.000 | k-means++ | NaN | 30 | NaN | 0.421 | 0.0 | 1.406 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.383 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.488 | 4.987 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.317 | 7.060 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.473 | 0.000 | 1.016 | 0.000 | random | NaN | 30 | NaN | 0.399 | 0.0 | 1.184 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.385 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.989 | 4.482 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.022 | 6.729 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.377 | 0.000 | 3.763 | 0.000 | k-means++ | NaN | 30 | NaN | 2.805 | 0.0 | 2.274 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 15.338 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.626 | 2.312 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.019 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.668 | 6.687 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.672 | 0.000 | 4.231 | 0.000 | random | NaN | 30 | NaN | 2.653 | 0.0 | 2.138 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.001 | 13.094 | 0.000 | random | 0.003 | 30 | 0.002 | 0.000 | 0.0 | 6.689 | 3.541 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.419 | 5.925 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.238 | 0.000 | 0.013 | 0.000 | k-means++ | NaN | 20 | NaN | 0.031 | 0.0 | 7.567 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.001 | 0.162 | 0.000 | k-means++ | -0.001 | 20 | -0.000 | 0.001 | 0.0 | 3.076 | 1.043 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.382 | 6.048 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.078 | 0.000 | 0.041 | 0.000 | random | NaN | 20 | NaN | 0.086 | 0.0 | 0.910 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.190 | 0.000 | random | -0.001 | 20 | 0.001 | 0.001 | 0.0 | 2.697 | 0.509 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.341 | 5.865 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.625 | 0.000 | 0.256 | 0.000 | k-means++ | NaN | 20 | NaN | 0.135 | 0.0 | 4.627 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.000 | 6.554 | 0.000 | k-means++ | 0.336 | 20 | 0.289 | 0.001 | 0.0 | 2.076 | 0.324 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.000 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.186 | 4.795 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.209 | 0.000 | 0.764 | 0.000 | random | NaN | 20 | NaN | 0.337 | 0.0 | 0.621 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.000 | 6.762 | 0.000 | random | 0.325 | 20 | 0.243 | 0.001 | 0.0 | 2.152 | 0.424 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.000 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.482 | 4.113 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 10.607 | 0.0 | [-0.11123046] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.769 | 0.0 | 5.995 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [54.32680767] | 0.000 | NaN | NaN | NaN | NaN | 0.524 | 0.000 | 0.0 | 0.884 | 0.400 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.26096544] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.360 | 0.339 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.719 | 0.0 | [2.89328651] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.739 | 0.0 | 0.973 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.001 | 0.0 | [139.24968653] | 0.000 | NaN | NaN | NaN | NaN | 0.280 | 0.003 | 0.0 | 0.534 | 0.100 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [26.10736653] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.115 | 0.086 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.170 | 0.0 | 0.471 | 0.0 | NaN | NaN | NaN | 0.185 | 0.0 | 0.918 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 8.326 | 0.0 | NaN | NaN | 0.093 | 0.016 | 0.0 | 0.590 | 0.008 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.215 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.582 | 0.542 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.263 | 0.0 | 0.633 | 0.0 | NaN | NaN | NaN | 0.227 | 0.0 | 5.566 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 2.384 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 1.475 | 1.505 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.012 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.701 | 0.735 | See | See |
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()